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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenyu Hu Baili Zhang |
| Copyright Year | 2012 |
| Description | Author affiliation: School of Computer Science and Engineering, Southeast University, Nanjing, China (Baili Zhang) || Department of Computer and Information Science, Fujian University of Technology, Fuzhou, China (Wenyu Hu) |
| Abstract | Sampling is the most versatile approximation technique available and is still one of the most powerful methods for building a one-pass synopsis of a data set in a streaming environment. Throughout the detailed review, a kind of taxonomic frame of sampling algorithms was presented; meanwhile, discussions and comparisons of representative sampling algorithms were performed. Due to the limitations of uniform sampling in some applications, the importance of using biased sampling methods in these scenarios was fully dissertated. Subsequently, we surveyed the application and development of sampling techniques, especially those traditional sampling techniques in data stream model. Finally, we discussed the research challenges and future directions of sampling problem in the context of data streams. |
| Starting Page | 1028 |
| Ending Page | 1034 |
| File Size | 401876 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781467300254 |
| e-ISBN | 9781467300247 |
| DOI | 10.1109/FSKD.2012.6234278 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-05-29 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis data stream mining uniform sampling Heuristic algorithms Reservoirs Approximation algorithms data-stream sampling synopsis data structure Classification algorithms Data mining Approximation methods biased sampling |
| Content Type | Text |
| Resource Type | Article |
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